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Randomized Algorithms CS648

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trevor
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Randomized Algorithms CS648. Lecture 13 Expected duration of a randomized experiment Part I. coupon Collector Problem. Coupon Collector Problem. There is a bag containing distinct coupons. Each coupon has a unique label from [ ]. Experiment: Repeat

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Randomized Algorithms CS648
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  1. Randomized AlgorithmsCS648 Lecture 13 Expected duration of a randomized experiment Part I

  2. coupon Collector Problem

  3. Coupon Collector Problem • There is a bag containing distinct coupons. • Each coupon has a unique label from []. Experiment: Repeat 1. Select a coupon randomly uniformly from the bag 2. Note down its label 3. Place the coupon back into the bag Until every coupon has appeared at least once : the number of iterations of the loop (number of coupons drawn). Question: What is E[] ?

  4. Example =5 1 4 5 2 3 5 3 5 2 2 1 2 3 1 3 3 2 1 5 4 1 4 5 1 1 3 2 5 3 3 2 3 1 3 3 1 2 2 1 5 1 3 5 3 4 Done in 14 samplings Done in 12 samplings 2 5

  5. Coupon Collector Problem : the number of iterations of the loop (number of coupons drawn). Question: What is E[] ? Standard method: E[] = ? No easy way !!

  6. Coupon Collector Problem no coupon seen all coupons seen This transition is not sudden. In fact it is a gradual transition through various discrete stages. Can you see these discrete stages ?

  7. Coupon Collector Problem no coupon seen all coupons seen 1 2 3 4 This transition is not sudden. In fact it is a gradual transition through various discrete stages. Can you see these discrete stages ?

  8. Reviewing Example =5 1 4 5 2 3 5 0 3 1 3 3 1 2 2 1 5 1 3 5 3 4

  9. Reviewing Example Each instance of coupon collector problem has to pass through these stages. Does it give you some inspiration to calculate E[X] ? =5 1 4 5 2 3 1 0 1 3 3 0 5 4 2 3 5 2 4 2 1 4 0 5 3 2 5 2 1 2 3 3 1 3 2 5 1 4 1 5 4 1 1 3 2 5 3 3 2 3 1 3 3 1 2 2 1 5 1 3 5 3 4 2 5

  10. Coupon Collector Problem : the number of iterations of the loop (number of coupons drawn). Question: What is E[] ? no. of coupons sampled from the moment ?? to the moment ?? th distinct coupon was selected th distinct coupon was selected

  11. Reviewing Example =5 1 4 5 2 3 1 0 5 4 3 2 =1 =1 =4 =3 =5 3 1 3 3 1 2 2 1 5 1 3 5 3 4 This picture validates the equality

  12. Coupon Collector Problem : the number of iterations of the loop (number of coupons drawn).  Question: What is ] ?

  13. Calculating E[] Experiment (in th stage): Repeat 1. Select a coupon randomly uniformly from the bag 2. Note down its label 3. Place the coupon back into the bag Untilth distinct coupon appears.

  14. Calculating E[] Experiment (in th stage): Repeat 1. Select a coupon randomly uniformly from the bag 2. Note down its label 3. Place the coupon back into the bag Untilth distinct coupon appears. =Probability an iteration is successful Question: What is ? E[] = = = =

  15. Coupon Collector Problem : the number of iterations of the loop (number of coupons drawn).  Theorem: Expected duration of coupon collector experiment is .

  16. Discrete Random Walk on a LINE

  17. Discrete Random Walk • Particle starts from origin • In each second, particle moves 1 unit to the left or to the right with equal probability. • While at origin, the particle moves to 1 always. Question: What is the expected number of steps of the random walk to reach milestone n? 0 1 2 3 4 5 6 7 8 … n n+1

  18. An example 0 1 2 3 4 5 6 7 8 … I,and perhaps you too, could not notice the walk. So let us trace the walk slowly.

  19. Formalism : No. of steps of a random walk which starts at and terminates on reaching for the first time. Aim: To calculate E[]

  20. Careful look at the example 0 1 2 3 4 5 6 7 8 … Can you break the walk08into stages ? Think carefully …

  21. Careful look at the example 0 1 2 3 4 5 6 7 8 … Walk starting from 0 and terminating at 5

  22. Careful look at the example 0 1 2 3 4 5 6 7 8 … Walk starting from 0 and terminating at 5 Walk starting from 5 and terminating at 8

  23. Relation among ’s For any = + Breaking down to the limits, we get = Hence using linearity of expectation E[] = ]

  24. Relation among ’s 0 1 2 3 4 5 6 7 8 … 1 1 3 1 5 1 5 11

  25. How to calculate E[] ?

  26. Conditional Expectation Given any event and a random variable defined over a probability space (,P). E[] = E[| ] P() + E[| ] P() E[| ] E[| ] Ω A useful tool to calculate expected value of a random variable

  27. Calculating E[] E[] = ?? = ½ E[| first move is L] + ½ . 1 = ½ E[| first move is L] + ½ 0 1 … +1 ½ E[| first move is L] + ½ E[| first move is R] ?

  28. Calculating E[| first move is L] E[| first move is L] = ?? = 1 + E[]+ E[]//by linearity of expectation 0 1 … +1 1 + E[]

  29. Calculating E[] E[] = ½ E[| first move is L] + ½ . 1 = ½ + ½ . 1 = 1 + ½ 2 E[] = 2 + ]  E[] = 2 + Question: What is E[] ? Question: What is E[] ? Question: What is E[] ? Answer: ?? 0 1 … +1 3

  30. Calculating E[]

  31. Calculating E[] Lemma (just proved): E[] = E[] = = = = = 0 1 2 3 4 5 6 7 … +1

  32. Theorem:Expected number of steps of a random walk starting from 0 and terminating on reaching is .

  33. Expected duration of a random experiment Let Xdenote the random variable for the duration of a randomized experiment. To calculate E[X], the following approach is sometimes useful: • Partitionthe experiment into stagescarefully. • Calculate expected duration of each stage. • Using linearity of expectation, calculate E[X]. In the next class, we shall discuss more non-trivial randomized algorithms which are analyzed using this method.